1. Technical Field
The present invention generally relates to wireless communication systems and in particular to transmission mode selection in wireless communication systems.
2. Description of the Related Art
Multiple-Input Multiple-Output (MIMO) systems are a primary enabler of the high data rate sought to be achieved by Long Term Evolution (LTE), an emerging 4G wireless access technology. Closed-Loop-Spatial-Multiplexing and Open-Loop-Spatial-Multiplexing are the two primary MIMO Transmission Modes used in the LTE downlink. In order to achieve satisfactory throughput performance, the choice of the most suitable MIMO Transmission Mode should not only depend on the measured signal quality at the mobile but also on additional factors such as the channel correlation and mobile speed.
In wireless communication systems, a base station selects a particular transmission mode and rank based on several factors, including the precoder and the Channel Quality Indicator (CQI) information reported by the mobile. Some of the other factors which are not reported by the mobile device and upon which the transmission mode and rank selection depends include (a) the relative speed between the mobile and the base station (Doppler) and (b) the channel correlation (multipath) between the base station and mobile antennas. Conventional approaches utilize complicated algorithms for heuristic estimation of Doppler and Multi-path to adjust the downlink transmission modes. Two particular approaches for solving the transmission mode and rank selection problem are the following: (a) at the mobile: CQI/PMI/RI (precoder matrix index/rank indicator) reporting is modified to be based not only on the measured Carrier to Interference (C/I) (ratio) but also on the observed channel correlation and Doppler; and (b) at the eNodeB: the correlation and Doppler are estimated at the eNodeB and the estimated information is used together with the reported CQI/PMI/RI to select a suitable transmission mode and rank. However, these solutions have the following problems: (1) the LTE standards do not impose any requirement on the mobile device to estimate the channel correlation and Doppler, so the mobile device based solution described in (a) above is not workable; (2) Estimation of correlation and speed require (a) fine measurements of the channel between the mobile and the eNodeB and (b) a significant level of computational and logical complexity; and (3) The most significant obstacle is the fact that there is no deterministic way to map the correlation and speed to the performance of specific transmission modes and rank. Additionally, the performance of transmission modes and rank also depends on the C/I operating point. Consequently, any such heuristic map would be very sensitive to at least three (3) input parameters, Doppler, channel correlation (multipath) and measured C/I. There can be other additional mobile device and eNodeB specific conditions that could influence the performance of the downlink Transmission Modes and Rank. Consequently, a scheme at the eNodeB (or the mobile device) to determine all the major causes affecting performance of Transmission Modes and selecting the most suitable Transmission Mode (TxMode) and Rank based on the estimated value for each of the major causes, would be complex and a heuristic guess at best.
Through empirical data, simulations and analysis, it has been observed that if an eNodeB automatically follows/fulfills the request by a mobile/wireless device and provides downlink communication transmission to the wireless device via the requested transmission mode and rank, then the achieved throughput would be suboptimal. There can even be regions of throughput inversion, in which regions the measured throughput reduces with an increase in C/I. Thus, suboptimal throughput is likely achieved if the eNodeB blindly follows wireless devices' inputs/indications for the best Transmission Mode and Rank pairing.